Spatial Analysis Of Interacting Economies

6. 2 Basic Model Characteristics 185 6. 3 A Closed Model Approach to Interregional Estimation 189 7 Towards an Integrated System of Models for National and Regional Development 205 7. 1 Introduction 205 7. 2 In Search of a Framework for Integration 207 7. 3 National Development Scenarios 222 7. 4 The National-Regional Interlace 231 7. 5 Regional Development Scenarios 236 7. 6 Concluding Remarks 244 Appendixes 253 A Basic Microstate Descriptions 253 B Incomplete Prior Information: A Simple Example 257 C Computing Capital Coefficients and Turnpike Solutions: The DYNIO Package 259 D Minimizing Information Losses in Simple Aggregation: Two Test Problems 274 E Computing Interregional and Intersectoral Flows: 276 References 287 Index 305 vi LIST OF FIGURES 1. 1 A Three-Dimensional Guide to Later Chapters 12 2. 1 Historical Development of the Entropy Concept 32 2. 2 Selected Applications of Information Theory to Input-Output Analysis and Interaction Modelling 48 3. 1 The Bose-Einstein Analogy 58 5. 1 The Dog-Leg Input-Output Table 159 7. 1 A General Multilevel Social System 219 7. 2 The Hierarchical System of Models 219 7. 3 Choice of Production Techniques 230 7. 4 The National-Regional Interface 235 7. 5 A Sequential Compromise Procedure 243 7. 6 The Integrated Modelling System 246 vii LIST OF TABLES 3. 1 Production-Constrained Microstate Descriptions 59 3. 2 Production-Constrained Entropy Formulae 62 3. 3 Production-Constrained Solutions 65 3. 4 Doubly-Constrained Solutions 73 4. 1 The Static Input-Output Table 85 4.

This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises. This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data.

Spatial data analysis has seen explosive growth in recent years. Both in mainstream statistics and econometrics as well as in many applied ?elds, the attention to space, location, and interaction has become an important feature of scholarly work. The methodsdevelopedto dealwith problemsofspatialpatternrecognition,spatialau- correlation, and spatial heterogeneity have seen greatly increased adoption, in part due to the availability of user friendlydesktopsoftware. Throughhis theoretical and appliedwork,ArthurGetishasbeena majorcontributing?gureinthisdevelopment. In this volume, we take both a retrospective and a prospective view of the ?eld. We use the occasion of the retirement and move to emeritus status of Arthur Getis to highlight the contributions of his work. In addition, we aim to place it into perspective in light of the current state of the art and future directions in spatial data analysis. To this end, we elected to combine reprints of selected classic contributions by Getiswithchapterswrittenbykeyspatialscientists.Thesescholarswerespeci?cally invited to react to the earlier work by Getis with an eye toward assessing its impact, tracing out the evolution of related research, and to re?ect on the future broadening of spatial analysis. The organizationof the book follows four main themes in Getis’ contributions: • Spatial analysis • Pattern analysis • Local statistics • Applications For each of these themes, the chapters provide a historical perspective on early methodological developments and theoretical insights, assessments of these c- tributions in light of the current state of the art, as well as descriptions of new techniques and applications.

This volume contains selected essays of Manfred M. Fischer in the field of spatial analysis from the perspective of GeoComputation. The volume is structured in four parts, from broad issues in spatial analysis and the role of GIS to computational intelligence technologies such as neural networks. The third part provides the theoretical framework required for adaptive pattern classifiers in remote sensing environments. The final section outlines the latest in neural spatial interaction modeling.

The Handbook is written for academics, researchers, practitioners and advanced graduate students. It has been designed to be read by those new or starting out in the field of spatial analysis as well as by those who are already familiar with the field. The chapters have been written in such a way that readers who are new to the field will gain important overview and insight. At the same time, those readers who are already practitioners in the field will gain through the advanced and/or updated tools and new materials and state-of-the-art developments included. This volume provides an accounting of the diversity of current and emergent approaches, not available elsewhere despite the many excellent journals and te- books that exist. Most of the chapters are original, some few are reprints from the Journal of Geographical Systems, Geographical Analysis, The Review of Regional Studies and Letters of Spatial and Resource Sciences. We let our contributors - velop, from their particular perspective and insights, their own strategies for m- ping the part of terrain for which they were responsible. As the chapters were submitted, we became the first consumers of the project we had initiated. We gained from depth, breadth and distinctiveness of our contributors’ insights and, in particular, the presence of links between them.

The availability of spatial databases and widespread use of geographic information systems has stimulated increasing interest in the analysis and modelling of spatial data. Spatial data analysis focuses on detecting patterns, and on exploring and modelling relationships between them in order to understand the processes responsible for their emergence. In this way, the role of space is emphasised , and our understanding of the working and representation of space, spatial patterns, and processes is enhanced. In applied research, the recognition of the spatial dimension often yields different and more meaningful results and helps to avoid erroneous conclusions. This book aims to provide an introduction into spatial data analysis to graduates interested in applied statistical research. The text has been structured from a data-driven rather than a theory-based perspective, and focuses on those models, methods and techniques which are both accessible and of practical use for graduate students. Exploratory techniques as well as more formal model-based approaches are presented, and both area data and origin-destination flow data are considered.

The concept of space has always been a fundamental element in various branches of knowledge. The concept often appears in the evolution of knowledge, either as a basis of theory or as a factor in research. It is associated, more or less directly, with all the history of scientific thought. At the level of simple common sense, the importance of the concept of space is only equaled by its lack of precision. It was part of legend before becoming part of history. To indicate the founding of Rome, Romulus started by drawing the boundaries, locating its landmarks in a discontinuous space after having cut the limits of a continuous space. However, neither geographical explorations nor mathematico-logical speculations have ever completely removed the mystery from the concept of space. For all its simple common sense, its mystique remains intact. The privileged position occupied by the concept of space in the history of science and the vagueness of its meaning in the current use of the term, far from constituting a paradox, are mutually explanatory. Every concept of space is necessarily the result of an abstraction, whether the process by which it is reached is through mathematics, psychology, biology, or any other discipline. At the level of common knowledge, the space-time concept is the base upon which are arranged individual experiences. It is thus easy to understand how the concept of space can be understood only through an orderly arrangement of these experiences and their integration into a logical scheme.

This book presents cutting‐edge research on urban and regional systems applying modern spatial analytical techniques of Geographic Information Science & Technologies (GIS&T), spatial statistics, and location modeling. The contributions, written by leading scholars from around the globe, adopt a spatially explicit analytical perspective and highlight methodological innovations and substantive breakthroughs on many facets of the socioeconomic and environmental reality of urban and regional contexts. The book is divided into three parts: The first part offers an introduction to the research field, while the second part discusses critical issues in urban growth and urban management, presenting case studies on city and urban environments, their growth, data infrastructures and spatial and management issues. The third part then broadens the analysis to the regional scale, addressing growth, convergence and adaptation to new economic and information‐based realities. This book appeals to scholars of spatial and regional sciences as well as to policy decision-makers interested in advanced methods of spatial analysis, location modeling, and GIS&T.